Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer.

INTRODUCTION: Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED: In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION: The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring.

Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. 'glszm Zone Entropy'), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical 'discovery' radiomics in the neuro-oncology space.

Health diplomacy in action: The cancer legacy of the Good Friday Agreement.

2023 marks the 25th anniversary of the Good Friday Agreement, which led peace in Northern Ireland. As well as its impact on peace and reconciliation, the Good Friday Agreement has also had a lasting positive impact on cancer research and cancer care across the island of Ireland. Pursuant to the Good Friday Agreement, a Memorandum of Understanding (MOU) was signed between the respective Departments of Health in Ireland, Northern Ireland and the US National Cancer Institute (NCI), giving rise to the Ireland - Northern Ireland - National Cancer Institute Cancer Consortium, an unparalleled tripartite agreement designed to nurture and develop linkages between cancer researchers, physicians and allied healthcare professionals across Ireland, Northern Ireland and the US, delivering world class research and better care for cancer patients on the island of Ireland and driving research and innovation in the US.

Cannabidiol Inhibits the Proliferation and Invasiveness of Prostate Cancer Cells.

Prostate cancer is the fifth leading cause of cancer death in men, responsible for over 375,000 deaths in 2020. Novel therapeutic strategies are needed to improve outcomes. Cannabinoids, chemical components of the cannabis plant, are a possible solution. Preclinical evidence demonstrates that cannabinoids can modulate several cancer hallmarks of many tumor types. However, the therapeutic potential of cannabinoids in prostate cancer has not yet been fully explored. The aim of this study was to investigate the antiproliferative and anti-invasive properties of cannabidiol (CBD) in prostate cancer cells in vitro. CBD inhibited cell viability and proliferation, accompanied by reduced expression of key cell cycle proteins, specifically cyclin D3 and cyclin-dependent kinases CDK2, CDK4, and CDK1, and inhibition of AKT phosphorylation. The effects of CBD on cell viability were not blocked by cannabinoid receptor antagonists, a transient receptor potential vanilloid 1 (TRPV1) channel blocker, or an agonist of the G-protein-coupled receptor GPR55, suggesting that CBD acts independently of these targets in prostate cancer cells. Furthermore, CBD reduced the invasiveness of highly metastatic PC-3 cells and increased protein expression of E-cadherin. The ability of CBD to inhibit prostate cancer cell proliferation and invasiveness suggests that CBD may have potential as a future chemotherapeutic agent.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

SRF inhibitors reduce prostate cancer cell proliferation through cell cycle arrest in an isogenic model of castrate-resistant prostate cancer.

Castrate-resistant prostate cancer (CRPC) is challenging to treat, despite improvements with next-generation anti-androgens such as enzalutamide, due to acquired resistance. One of the mechanisms of such resistance includes aberrant activation of co-factors of the androgen receptor (AR), such as the serum response factor (SRF), which was associated with prostate cancer progression and resistance to enzalutamide. Here, we show that inhibition of SRF with three small molecules (CCG-1423, CCG-257081 and lestaurtinib), singly and in combination with enzalutamide, reduces cell viability in an isogenic model of CRPC. The effects of these inhibitors on the cell cycle, singly and in combination with enzalutamide, were assessed with western blotting, flow cytometry and ß-galactosidase staining. In the androgen deprivation-sensitive LNCaP parental cell line, a synergistic effect between enzalutamide and all three inhibitors was demonstrated, while the androgen deprivation-resistant LNCaP Abl cells showed synergy only with the lestaurtinib and enzalutamide combination, suggesting a different mechanism of action of the CCG series of compounds in the absence and presence of androgens. Through analysis of cell cycle checkpoint proteins, flow cytometry and ß-galactosidase staining, we showed that all three SRF inhibitors, singly and in combination with enzalutamide, induced cell cycle arrest and decreased S phase. While CCG-1423 had a more pronounced effect on the expression of cell cycle checkpoint proteins, CCG-257081 and lestaurtinib decreased proliferation also through induction of cellular senescence. In conclusion, we show that inhibition of an AR co-factors, namely SRF, provides a promising approach to overcoming resistance to AR inhibitors currently used in the clinic.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer.

BACKGROUND: Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease. OBJECTIVE: To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. DESIGN, SETTING, AND PARTICIPANTS: Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models. RESULTS AND LIMITATIONS: The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study. CONCLUSIONS: The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP. PATIENT SUMMARY: We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Mapping the Immune Landscape in Metastatic Melanoma Reveals Localized Cell-Cell Interactions That Predict Immunotherapy Response.

While immune checkpoint-based immunotherapy (ICI) shows promising clinical results in patients with cancer, only a subset of patients responds favorably. Response to ICI is dictated by complex networks of cellular interactions between malignant and nonmalignant cells. Although insights into the mechanisms that modulate the pivotal antitumoral activity of cytotoxic T cells (Tcy) have recently been gained, much of what has been learned is based on single-cell analyses of dissociated tumor samples, resulting in a lack of critical information about the spatial distribution of relevant cell types. Here, we used multiplexed IHC to spatially characterize the immune landscape of metastatic melanoma from responders and nonresponders to ICI. Such high-dimensional pathology maps showed that Tcy gradually evolve toward an exhausted phenotype as they approach and infiltrate the tumor. Moreover, a key cellular interaction network functionally linked Tcy and PD-L1+ macrophages. Mapping the respective spatial distributions of these two cell populations predicted response to anti-PD-1 immunotherapy with high confidence. These results suggest that baseline measurements of the spatial context should be integrated in the design of predictive biomarkers to identify patients likely to benefit from ICI. SIGNIFICANCE: This study shows that spatial characterization can address the challenge of finding efficient biomarkers, revealing that localization of macrophages and T cells in melanoma predicts patient response to ICI. See related commentary by Smalley and Smalley, p. 3198.

Implementing subtype-specific pre-clinical models of breast cancer to study pre-treatment aspirin effects.

BACKGORUND: Prior data suggest pre-diagnostic aspirin use impacts breast tumour biology and patient outcome. Here, we employed faithful surgical resection models of HER2+ and triple-negative breast cancer (TNBC), to study outcome and response mechanisms across breast cancer subtypes. METHOD: NOD/SCID mice were implanted with HER2+ MDA-MB-231/LN/2-4/H2N, trastuzumab-resistant HER2+ HCC1954 or a TNBC patient-derived xenograft (PDX). A daily low-dose aspirin regimen commenced until primary tumours reached ~250 mm3 and subsequently resected. MDA-MB-231/LN/2-4/H2N mice were monitored for metastasis utilising imaging. To interrogate the survival benefit of pre-treatment aspirin, 3 weeks post-resection, HCC1954/TNBC animals received standard-of-care (SOC) chemotherapy for 6 weeks. Primary tumour response to aspirin was interrogated using immunohistochemistry. RESULTS: Aspirin delayed time to metastasis in MDA-MB-231/LN/2-4/H2N xenografts and decreased growth of HER2+ /TNBC primary tumours. Lymphangiogenic factors and lymph vessels number were decreased in HER2+ tumours. However, no survival benefit was seen in aspirin pre-treated animals (HCC1954/TNBC) that further received adjuvant SOC, compared with animals treated with SOC alone. In an effort to study mechanisms responsible for the observed reduction in lymphangiogenesis in HER2+ BC we utilised an in vitro co-culture system of HCC1954 tumour cells and mesenchymal stromal cells (MSC). Aspirin abrogated the secretion of VEGF-C in MSCs and also decreased the lymph/angiogenic potential of the MSCs and HCC1954 by tubule formation assay. Furthermore, aspirin decreased the secretion of uPA in HCC1954 cells potentially diminishing its metastatic capability. CONCLUSION: Our data employing clinically relevant models demonstrate that aspirin alters breast tumour biology. However, aspirin may not represent a robust chemo-preventative agent in the HER2+ or TNBC setting.

Plant-derived cannabinoids as anticancer agents.

Substantial preclinical evidence demonstrates the antiproliferative, cytotoxic, and antimetastatic properties of plant-derived cannabinoids (phytocannabinoids) such as cannabidiol and tetrahydrocannabinol. The cumulative body of research into the intracellular mechanisms and phenotypic effects of these compounds supports a logical, judicious progression to large-scale phase II/III clinical trials in certain cancer types to truly assess the efficacy of phytocannabinoids as anticancer agents.

Analytical Validation of a Novel 6-Gene Signature for Prediction of Distant Recurrence in Estrogen Receptor-Positive, HER2-Negative, Early-Stage Breast Cancer.

BACKGROUND: OncoMasTR is a recently developed multigene prognostic test for early-stage breast cancer. The test has been developed in a kit-based format for decentralized deployment in molecular pathology laboratories. The analytical performance characteristics of the OncoMasTR test are described in this study. METHODS: Expression levels of 6 genes were measured by 1-step reverse transcription-quantitative PCR on RNA samples prepared from formalin-fixed, paraffin-embedded (FFPE) breast tumor specimens. Assay precision, reproducibility, input range, and interference were determined using FFPE-derived RNA samples representative of low and high prognostic risk scores. A pooled RNA sample derived from 6 FFPE breast tumor specimens was used to establish the linear range, limit of detection, and amplification efficiency of the individual gene expression assays. RESULTS: The overall precision of the OncoMasTR test was high with an SD of 0.16, which represents less than 2% of the 10-unit risk score range. Test results were reproducible across 4 testing sites, with correlation coefficients of 0.94 to 0.96 for the continuous risk score and concordance of 86% to 96% in low-/high-risk sample classification. Consistent risk scores were obtained across a > 100-fold RNA input range. Individual gene expression assays were linear up to quantification cycle values of 36.0 to 36.9, with amplification efficiencies of 80% to 102%. Test results were not influenced by agents used during RNA isolation, by low levels of copurified genomic DNA, or by moderate levels of copurified adjacent nontumor tissue. CONCLUSION: The OncoMasTR prognostic test displays robust analytical performance that is suitable for deployment by local pathology laboratories for decentralized use.

Pre-clinical and clinical studies on the role of RBM3 in muscle-invasive bladder cancer: longitudinal expression, transcriptome-level effects and modulation of chemosensitivity.

BACKGROUND: The response to neoadjuvant cisplatin-based chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is impaired in up to 50% of patients due to chemoresistance, with no predictive biomarkers in clinical use. The proto-oncogene RNA-binding motif protein 3 (RBM3) has emerged as a putative modulator of chemotherapy response in several solid tumours but has a hitherto unrecognized role in MIBC. METHODS: RBM3 protein expression level in tumour cells was assessed via immunohistochemistry in paired transurethral resection of the bladder (TURB) specimens, cystectomy specimens and lymph node metastases from a consecutive cohort of 145 patients, 65 of whom were treated with NAC. Kaplan-Meier and Cox regression analyses were applied to estimate the impact of RBM3 expression on time to recurrence (TTR), cancer-specific survival (CSS), and overall survival (OS) in strata according to NAC treatment. The effect of siRNA-mediated silencing of RBM3 on chemosensitivity was examined in RT4 and T24 human bladder carcinoma cells in vitro. Cellular functions of RBM3 were assessed using RNA-sequencing and gene ontology analysis, followed by investigation of cell cycle distribution using flow cytometry. RESULTS: RBM3 protein expression was significantly higher in TURB compared to cystectomy specimens but showed consistency between primary tumours and lymph node metastases. Patients with high-tumour specific RBM3 expression treated with NAC had a significantly reduced risk of recurrence and a prolonged CSS and OS compared to NAC-untreated patients. In high-grade T24 carcinoma cells, which expressed higher RBM3 mRNA levels compared to RT4 cells, RBM3 silencing conferred a decreased sensitivity to cisplatin and gemcitabine. Transcriptomic analysis revealed potential involvement of RBM3 in facilitating cell cycle progression, in particular G1/S-phase transition, and initiation of DNA replication. Furthermore, siRBM3-transfected T24 cells displayed an accumulation of cells residing in the G1-phase as well as altered levels of recognised regulators of G1-phase progression, including Cyclin D1/CDK4 and CDK2. CONCLUSIONS: The presented data highlight the potential value of RBM3 as a predictive biomarker of chemotherapy response in MIBC, which could, if prospectively validated, improve treatment stratification of patients with this aggressive disease.

Emerging role for the Serum Response Factor (SRF) as a potential therapeutic target in cancer.

INTRODUCTION: The Serum Response Factor (SRF) is a transcription factor involved in three hallmarks of cancer: the promotion of cell proliferation, cell death resistance and invasion and metastasis induction. Many studies have demonstrated a leading role in the development and progression of multiple cancer types, thus highlighting the potential of SRF as a prognostic biomarker and therapeutic target, especially for cancers with poor prognosis. AREAS COVERED: This review examines the role of SRF in several cancers in promoting cellular processes associated with cancer development and progression. SRF co-factors and signaling pathways are discussed as possible targets to inhibit SRF in a tissue and cancer-specific way. Small-molecule inhibitors of SRF, such as the CCGs series of compounds and lestaurtinib, which could be used as cancer therapeutics, are also discussed. EXPERT OPINION: Targeting of SRF and its co-factors represents a promising therapeutic approach. Further understanding of the molecular mechanisms behind the action of SRF could provide a pipeline of novel molecular targets and therapeutic combinations for cancer. Basket clinical trials and the use of SRF immunohistochemistry as companion diagnostics will help testing of these new targets in patients.

Identifying the Steps Required to Effectively Implement Next-Generation Sequencing in Oncology at a National Level in Europe.

Next-generation sequencing (NGS) may enable more focused and highly personalized cancer treatment, with the National Comprehensive Cancer Network and European Society for Medical Oncology guidelines now recommending NGS for daily clinical practice for several tumor types. However, NGS implementation, and therefore patient access, varies across Europe; a multi-stakeholder collaboration is needed to establish the conditions required to improve this discrepancy. In that regard, we set up European Alliance for Personalised Medicine (EAPM)-led expert panels during the first half of 2021, including key stakeholders from across 10 European countries covering medical, economic, patient, industry, and governmental expertise. We describe the outcomes of these panels in order to define and explore the necessary conditions for NGS implementation into routine clinical care to enable patient access, identify specific challenges in achieving them, and make short- and long-term recommendations. The main challenges identified relate to the demand for NGS tests (governance, clinical standardization, and awareness and education) and supply of tests (equitable reimbursement, infrastructure for conducting and validating tests, and testing access driven by evidence generation). Recommendations made to resolve each of these challenges should aid multi-stakeholder collaboration between national and European initiatives, to complement, support, and mutually reinforce efforts to improve patient care.

1,4-dihydroxy quininib modulates the secretome of uveal melanoma tumour explants and a marker of oxidative phosphorylation in a metastatic xenograft model.

Uveal melanoma (UM) is an intraocular cancer with propensity for liver metastases. The median overall survival (OS) for metastatic UM (MUM) is 1.07 years, with a reported range of 0.84-1.34. In primary UM, high cysteinyl leukotriene receptor 1 (CysLT1) expression associates with poor outcomes. CysLT1 antagonists, quininib and 1,4-dihydroxy quininib, alter cancer hallmarks of primary and metastatic UM cell lines in vitro. Here, the clinical relevance of CysLT receptors and therapeutic potential of quininib analogs is elaborated in UM using preclinical in vivo orthotopic xenograft models and ex vivo patient samples. Immunohistochemical staining of an independent cohort (n = 64) of primary UM patients confirmed high CysLT1 expression significantly associates with death from metastatic disease (p = 0.02; HR 2.28; 95% CI 1.08-4.78), solidifying the disease relevance of CysLT1 in UM. In primary UM samples (n = 11) cultured as ex vivo explants, 1,4-dihydroxy quininib significantly alters the secretion of IL-13, IL-2, and TNF-α. In an orthotopic, cell line-derived xenograft model of MUM, 1,4-dihydroxy quininib administered intraperitoneally at 25 mg/kg significantly decreases ATP5B expression (p = 0.03), a marker of oxidative phosphorylation. In UM, high ATP5F1B is a poor prognostic indicator, whereas low ATP5F1B, in combination with disomy 3, correlates with an absence of metastatic disease in the TCGA-UM dataset. These preclinical data highlight the diagnostic potential of CysLT1 and ATP5F1B in UM, and the therapeutic potential of 1,4-dihydroxy quininib with ATP5F1B as a companion diagnostic to treat MUM.

PRR11 unveiled as a top candidate biomarker within the RBM3-regulated transcriptome in pancreatic cancer.

The outlook for patients with pancreatic cancer remains dismal. Treatment options are limited and chemotherapy remains standard of care, leading to only modest survival benefits. Hence, there is a great need to further explore the mechanistic basis for the intrinsic therapeutic resistance of this disease, and to identify novel predictive biomarkers. RNA-binding motif protein 3 (RBM3) has emerged as a promising biomarker of disease severity and chemotherapy response in several types of cancer, including pancreatic cancer. The aim of this study was to unearth RBM3-regulated genes and proteins in pancreatic cancer cells in vitro, and to examine their expression and prognostic significance in human tumours. Next-generation RNA sequencing was applied to compare transcriptomes of MIAPaCa-2 cells with and without RBM3 knockdown. The prognostic value of differentially expressed genes (DEGs) was examined in The Cancer Genome Atlas (TCGA). Top deregulated genes were selected for further studies in vitro and for immunohistochemical analysis of corresponding protein expression in tumours from a clinically well-annotated consecutive cohort of 46 patients with resected pancreatic cancer. In total, 19 DEGs (p < 0.01) were revealed, among which some with functions in cell cycle and cell division stood out; PDS5A (PDS cohesin associated factor A) as the top downregulated gene, CCND3 (cyclin D3) as the top upregulated gene, and PRR11 (proline rich 11) as being highly prognostic in TCGA. Silencing of RBM3 in MiaPaCa-2 cells led to congruent alterations of PDS5A, cyclin D3, and PRR11 levels. High protein expression of PRR11 was associated with adverse clinicopathological features and shorter overall survival. Neither PDS5A nor cyclin D3 protein expression was prognostic. This study unveils several RBM3-regulated genes with potential clinical relevance in pancreatic cancer, among which PRR11 shows the most consistent association with disease severity, at both transcriptome and protein levels.

Association of circulating short chain fatty acid levels with colorectal adenomas and colorectal cancer.

BACKGROUND & AIMS: Short chain fatty acid (SCFAs) are bacterially derived metabolites suggested to have protective roles against colorectal cancer (CRC) development. However, there is sparse evidence from epidemiological studies in this context. Here, we assessed whether circulating SCFA concentrations varied in patients with colorectal adenomas (CRA) and CRC. METHODS: Levels of seven SCFAs were extracted from plasma samples and determined by gas chromatography for 213 individuals from Ireland and the Czech Republic (CRC, n = 84; CRA, n = 66; controls, n = 63). RESULTS: In the Irish CRA/CRC cohort, only levels of 2-MethylButyric acid were significantly higher in cancers compared to the adenoma and control groups (p-values = 0.016 and 0.043). Using regression analysis, we observed that levels of Acetic and Propionic acid were associated with an increased CRC risk in the Czech cohort (Odd Ratio (OR): 1.02; 95% Confidence interval (CI): 1.00-1.03; OR: 1.29; 95% CI: 1.05-1.59, respectively), while i-Valeric and Valeric acid levels were associated with a decreased cancer risk (OR: 0.92; 95% CI: 0.86-0.99; OR: 0.67; 95% CI: 0.44-1.00). In the Irish cohort, levels of SCFAs were not associated with CRC risk. CONCLUSIONS: The association with colorectal neoplasia varied between the studied SCFAs. Future studies need to confirm these findings and address the mechanism of how these acids may promote or prevent colorectal carcinogenesis.